{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hub\n",
    "import torch\n",
    "import numpy as np\n",
    "from hub.schema import Image, ClassLabel\n",
    "from hub.utils import Timer\n",
    "from torchvision import transforms\n",
    "import albumentations as A\n",
    "from albumentations.pytorch import ToTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = hub.load(\"activeloop/mnist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting as is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_torch = ds.to_pytorch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['image', 'label'])\n",
      "torch.Size([28, 28, 1])\n"
     ]
    }
   ],
   "source": [
    "print(mnist_torch[0].keys())\n",
    "print(mnist_torch[0]['image'].size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each sample of dataset, converted to pytorch with default parameters, is a dictionary with keys 'image' and 'label', shape of images is [28, 28, 1] -> H, W, C. Note, that this is not the order used in pytorch (should be C x H x W). We will try to output list and tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0]]], dtype=torch.uint8), tensor(2)]\n"
     ]
    }
   ],
   "source": [
    "mnist_torch = ds.to_pytorch(output_type=list)\n",
    "print(mnist_torch[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         [ 56],\n",
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      "         [  0],\n",
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      "         [  0],\n",
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      "\n",
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      "\n",
      "        [[  0],\n",
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      "         [  0],\n",
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      "         [  0],\n",
      "         [  0],\n",
      "         [  0]],\n",
      "\n",
      "        [[  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
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      "\n",
      "        [[  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
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      "\n",
      "        [[  0],\n",
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      "         [  0],\n",
      "         [  0],\n",
      "         [  0],\n",
      "         [  0]]], dtype=torch.uint8), tensor(2))\n"
     ]
    }
   ],
   "source": [
    "mnist_torch = ds.to_pytorch(output_type=tuple)\n",
    "print(mnist_torch[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting with Transforms  - TorchVision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define torchvision transformations as a function. The function should work with initial dataset structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "torchvision_transform = transforms.Compose([\n",
    "    transforms.ToPILImage(),\n",
    "    transforms.Resize((256, 256)), \n",
    "    transforms.RandomCrop(224),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(\n",
    "        mean=(0.1307,), # mean value for mnist dataset\n",
    "        std=(0.3081,), # mean value for mnist dataset\n",
    "    )\n",
    "])\n",
    "\n",
    "def transform(data):\n",
    "    img = torchvision_transform(data['image'])\n",
    "    label = torch.tensor(data['label'])\n",
    "    return img, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `inplace=False` since we want to tranform original numpy arrays. Note, that now transform function defines output type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[[-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         ...,\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242]]]), tensor(2))\n"
     ]
    }
   ],
   "source": [
    "mnist_torch = ds.to_pytorch(Transform=transform, inplace=False)\n",
    "\n",
    "print(mnist_torch[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting with Albumentations  - TorchVision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "albumentations_transform = A.Compose([\n",
    "    A.Resize(256, 256), \n",
    "    A.RandomCrop(224, 224),\n",
    "    A.HorizontalFlip(),\n",
    "    A.Normalize(\n",
    "        mean=(0.1307,),\n",
    "        std=(0.3081,),\n",
    "    ),\n",
    "    ToTensor()\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(data):\n",
    "    augmented = albumentations_transform(image=data['image'])\n",
    "    img = augmented['image']\n",
    "    label = torch.tensor(data['label'])\n",
    "    return img, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_torch = ds.to_pytorch(Transform=transform, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[[-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         ...,\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242],\n",
      "         [-0.4242, -0.4242, -0.4242,  ..., -0.4242, -0.4242, -0.4242]]]), tensor(2))\n"
     ]
    }
   ],
   "source": [
    "print(mnist_torch[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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